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Ataei A, Amini A, Ghazizadeh A. Robust memory of face moral values is encoded in the human caudate tail: a simultaneous EEG-fMRI study. Sci Rep 2024; 14:12629. [PMID: 38824168 PMCID: PMC11144224 DOI: 10.1038/s41598-024-63085-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 05/24/2024] [Indexed: 06/03/2024] Open
Abstract
Moral judgements about people based on their actions is a key component that guides social decision making. It is currently unknown how positive or negative moral judgments associated with a person's face are processed and stored in the brain for a long time. Here, we investigate the long-term memory of moral values associated with human faces using simultaneous EEG-fMRI data acquisition. Results show that only a few exposures to morally charged stories of people are enough to form long-term memories a day later for a relatively large number of new faces. Event related potentials (ERPs) showed a significant differentiation of remembered good vs bad faces over centerofrontal electrode sites (value ERP). EEG-informed fMRI analysis revealed a subcortical cluster centered on the left caudate tail (CDt) as a correlate of the face value ERP. Importantly neither this analysis nor a conventional whole-brain analysis revealed any significant coding of face values in cortical areas, in particular the fusiform face area (FFA). Conversely an fMRI-informed EEG source localization using accurate subject-specific EEG head models also revealed activation in the left caudate tail. Nevertheless, the detected caudate tail region was found to be functionally connected to the FFA, suggesting FFA to be the source of face-specific information to CDt. A further psycho-physiological interaction analysis also revealed task-dependent coupling between CDt and dorsomedial prefrontal cortex (dmPFC), a region previously identified as retaining emotional working memories. These results identify CDt as a main site for encoding the long-term value memories of faces in humans suggesting that moral value of faces activates the same subcortical basal ganglia circuitry involved in processing reward value memory for objects in primates.
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Affiliation(s)
- Ali Ataei
- EE Department, Sharif University of Technology, Azadi Avenue, Tehran, 1458889694, Iran
- Sharif Brain Center, Sharif University of Technology, Tehran, Iran
| | - Arash Amini
- EE Department, Sharif University of Technology, Azadi Avenue, Tehran, 1458889694, Iran
| | - Ali Ghazizadeh
- EE Department, Sharif University of Technology, Azadi Avenue, Tehran, 1458889694, Iran.
- Sharif Brain Center, Sharif University of Technology, Tehran, Iran.
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran.
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2
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Brookshire G, Kasper J, Blauch NM, Wu YC, Glatt R, Merrill DA, Gerrol S, Yoder KJ, Quirk C, Lucero C. Data leakage in deep learning studies of translational EEG. Front Neurosci 2024; 18:1373515. [PMID: 38765672 PMCID: PMC11099244 DOI: 10.3389/fnins.2024.1373515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/04/2024] [Indexed: 05/22/2024] Open
Abstract
A growing number of studies apply deep neural networks (DNNs) to recordings of human electroencephalography (EEG) to identify a range of disorders. In many studies, EEG recordings are split into segments, and each segment is randomly assigned to the training or test set. As a consequence, data from individual subjects appears in both the training and the test set. Could high test-set accuracy reflect data leakage from subject-specific patterns in the data, rather than patterns that identify a disease? We address this question by testing the performance of DNN classifiers using segment-based holdout (in which segments from one subject can appear in both the training and test set), and comparing this to their performance using subject-based holdout (where all segments from one subject appear exclusively in either the training set or the test set). In two datasets (one classifying Alzheimer's disease, and the other classifying epileptic seizures), we find that performance on previously-unseen subjects is strongly overestimated when models are trained using segment-based holdout. Finally, we survey the literature and find that the majority of translational DNN-EEG studies use segment-based holdout. Most published DNN-EEG studies may dramatically overestimate their classification performance on new subjects.
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Affiliation(s)
| | - Jake Kasper
- SPARK Neuro Inc., New York, NY, United States
| | - Nicholas M. Blauch
- SPARK Neuro Inc., New York, NY, United States
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, United States
| | | | - Ryan Glatt
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, United States
| | - David A. Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, United States
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, United States
- Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, United States
| | | | | | - Colin Quirk
- SPARK Neuro Inc., New York, NY, United States
| | - Ché Lucero
- SPARK Neuro Inc., New York, NY, United States
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3
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Rosen D, Oh Y, Chesebrough C, Zhang FZ, Kounios J. Creative flow as optimized processing: Evidence from brain oscillations during jazz improvisations by expert and non-expert musicians. Neuropsychologia 2024; 196:108824. [PMID: 38387554 DOI: 10.1016/j.neuropsychologia.2024.108824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
Using a creative production task, jazz improvisation, we tested alternative hypotheses about the flow experience: (A) that it is a state of domain-specific processing optimized by experience and characterized by minimal interference from task-negative default-mode network (DMN) activity versus (B) that it recruits domain-general task-positive DMN activity supervised by the fronto-parietal control network (FPCN) to support ideation. We recorded jazz guitarists' electroencephalograms (EEGs) while they improvised to provided chord sequences. Their flow-states were measured with the Core Flow State Scale. Flow-related neural sources were reconstructed using SPM12. Over all musicians, high-flow (relative to low-flow) improvisations were associated with transient hypofrontality. High-experience musicians' high-flow improvisations showed reduced activity in posterior DMN nodes. Low-experience musicians showed no flow-related DMN or FPCN modulation. High-experience musicians also showed modality-specific left-hemisphere flow-related activity while low-experience musicians showed modality-specific right-hemisphere flow-related deactivations. These results are consistent with the idea that creative flow represents optimized domain-specific processing enabled by extensive practice paired with reduced cognitive control.
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Affiliation(s)
- David Rosen
- Department of Psychological and Brain Sciences, Drexel University, United States.
| | - Yongtaek Oh
- Department of Psychological and Brain Sciences, Drexel University, United States.
| | | | - Fengqing Zoe Zhang
- Department of Psychological and Brain Sciences, Drexel University, United States.
| | - John Kounios
- Department of Psychological and Brain Sciences, Drexel University, United States.
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Catrambone V, Candia‐Rivera D, Valenza G. Intracortical brain-heart interplay: An EEG model source study of sympathovagal changes. Hum Brain Mapp 2024; 45:e26677. [PMID: 38656080 PMCID: PMC11041380 DOI: 10.1002/hbm.26677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/18/2024] [Accepted: 03/23/2024] [Indexed: 04/26/2024] Open
Abstract
The interplay between cerebral and cardiovascular activity, known as the functional brain-heart interplay (BHI), and its temporal dynamics, have been linked to a plethora of physiological and pathological processes. Various computational models of the brain-heart axis have been proposed to estimate BHI non-invasively by taking advantage of the time resolution offered by electroencephalograph (EEG) signals. However, investigations into the specific intracortical sources responsible for this interplay have been limited, which significantly hampers existing BHI studies. This study proposes an analytical modeling framework for estimating the BHI at the source-brain level. This analysis relies on the low-resolution electromagnetic tomography sources localization from scalp electrophysiological recordings. BHI is then quantified as the functional correlation between the intracortical sources and cardiovascular dynamics. Using this approach, we aimed to evaluate the reliability of BHI estimates derived from source-localized EEG signals as compared with prior findings from neuroimaging methods. The proposed approach is validated using an experimental dataset gathered from 32 healthy individuals who underwent standard sympathovagal elicitation using a cold pressor test. Additional resting state data from 34 healthy individuals has been analysed to assess robustness and reproducibility of the methodology. Experimental results not only confirmed previous findings on activation of brain structures affecting cardiac dynamics (e.g., insula, amygdala, hippocampus, and anterior and mid-cingulate cortices) but also provided insights into the anatomical bases of brain-heart axis. In particular, we show that the bidirectional activity of electrophysiological pathways of functional brain-heart communication increases during cold pressure with respect to resting state, mainly targeting neural oscillations in theδ $$ \delta $$ ,β $$ \beta $$ , andγ $$ \gamma $$ bands. The proposed approach offers new perspectives for the investigation of functional BHI that could also shed light on various pathophysiological conditions.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory & Department of Information Engineering & Bioengineering and Robotics Research Center, E. Piaggio, School of EngineeringUniversity of PisaPisaItaly
| | - Diego Candia‐Rivera
- Sorbonne Université, Paris Brain Institute (ICM), INRIA, CNRS, INSERM, AP‐HP, Hôpital Pitié‐SalpêtriŕeParisFrance
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory & Department of Information Engineering & Bioengineering and Robotics Research Center, E. Piaggio, School of EngineeringUniversity of PisaPisaItaly
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Teng CL, Cong L, Wang W, Cheng S, Wu M, Dang WT, Jia M, Ma J, Xu J, Hu WD. Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis. Front Hum Neurosci 2024; 18:1338765. [PMID: 38415279 PMCID: PMC10897049 DOI: 10.3389/fnhum.2024.1338765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 01/25/2024] [Indexed: 02/29/2024] Open
Abstract
Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.
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Affiliation(s)
- Chao-Lin Teng
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, China
| | - Lin Cong
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, China
| | - Wei Wang
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Shan Cheng
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, China
| | - Min Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Wei-Tao Dang
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, China
| | - Min Jia
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jin Ma
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, China
| | - Jin Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Wen-Dong Hu
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi, China
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Karittevlis C, Papadopoulos M, Lima V, Orphanides GA, Tiwari S, Antonakakis M, Papadopoulou Lesta V, Ioannides AA. First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation. Front Syst Neurosci 2024; 17:1305022. [PMID: 38250330 PMCID: PMC10797085 DOI: 10.3389/fnsys.2023.1305022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 12/06/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation. Methods We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory. Results Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters. Discussion It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.
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Affiliation(s)
- Christodoulos Karittevlis
- AAI Scientific Cultural Services Ltd., Nicosia, Cyprus
- Department of Computer Science, European University Cyprus, Nicosia, Cyprus
| | | | - Vinicius Lima
- Aix Marseille Université, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
| | - Gregoris A. Orphanides
- AAI Scientific Cultural Services Ltd., Nicosia, Cyprus
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Shubham Tiwari
- Department of Geography, Durham University, Durham, United Kingdom
| | - Marios Antonakakis
- School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece
- Institute for Biomagnetism and Biosignal Analysis, Medicine Faculty, University of Münster, Münster, Germany
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7
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Riehm CD, Zuleger T, Diekfuss JA, Arellano E, Myer GD. The Evolution of Neuroimaging Technologies to Evaluate Neural Activity Related to Knee Pain and Injury Risk. Curr Rev Musculoskelet Med 2024; 17:14-22. [PMID: 38109007 PMCID: PMC10766917 DOI: 10.1007/s12178-023-09877-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/26/2023] [Indexed: 12/19/2023]
Abstract
PURPOSE OF REVIEW In this review, we present recent findings and advancements in the use of neuroimaging to evaluate neural activity relative to ACL injury risk and patellofemoral pain. In particular, we describe prior work using fMRI and EEG that demonstrate the value of these techniques as well as the necessity of continued development in this area. Our goal is to support future work by providing guidance for the successful application of neuroimaging techniques that most effectively expose pain and injury mechanisms. RECENT FINDINGS Recent studies that utilized both fMRI and EEG indicate that athletes who are at risk for future ACL injury exhibit divergent brain activity both during active lower extremity movement and at rest. Such activity patterns are likely due to alterations to cognitive, visual, and attentional processes that manifest as coordination deficits during naturalistic movement that may result in higher risk of injury. Similarly, in individuals with PFP altered brain activity in a number of key regions is related to subjective pain judgements as well as measures of fear of movement. Although these findings may begin to allow objective pain assessment and identification, continued refinement is needed. One key limitation across both ACL and PFP related work is the restriction of movement during fMRI and EEG data collection, which drastically limits ecological validity. Given the lack of sufficient research using EEG and fMRI within a naturalistic setting, our recommendation is that researchers target the use of mobile, source localized EEG as a primary methodology for exposing neural mechanisms of ACL injury risk and PFP. Our contention is that this method provides an optimal balance of spatial and temporal resolution with ecological validity via naturalistic movement.
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Affiliation(s)
- Christopher D Riehm
- Emory Sports Performance And Research Center (SPARC), 4450 Falcon Pkwy, Flowery Branch, GA, 30542, USA.
- Emory Sports Medicine Center, Atlanta, GA, USA.
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA.
| | - Taylor Zuleger
- Emory Sports Performance And Research Center (SPARC), 4450 Falcon Pkwy, Flowery Branch, GA, 30542, USA
- Emory Sports Medicine Center, Atlanta, GA, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
- Neuroscience Graduate Program, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Jed A Diekfuss
- Emory Sports Performance And Research Center (SPARC), 4450 Falcon Pkwy, Flowery Branch, GA, 30542, USA
- Emory Sports Medicine Center, Atlanta, GA, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
| | - Emilio Arellano
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
| | - Gregory D Myer
- Emory Sports Performance And Research Center (SPARC), 4450 Falcon Pkwy, Flowery Branch, GA, 30542, USA
- Emory Sports Medicine Center, Atlanta, GA, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
- Youth Physical Development Centre, Cardiff Metropolitan University, Wales, UK
- The Micheli Center for Sports Injury Prevention, Waltham, MA, USA
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Everitt A, Richards H, Song Y, Smith J, Kobylarz E, Lukovits T, Halter R, Murphy E. EEG electrode localization with 3D iPhone scanning using point-cloud electrode selection (PC-ES). J Neural Eng 2023; 20:066033. [PMID: 38055968 DOI: 10.1088/1741-2552/ad12db] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 12/06/2023] [Indexed: 12/08/2023]
Abstract
Objective.Electroencephalography source imaging (ESI) is a valuable tool in clinical evaluation for epilepsy patients but is underutilized in part due to sensitivity to anatomical modeling errors. Accurate localization of scalp electrodes is instrumental to ESI, but existing localization devices are expensive and not portable. As a result, electrode localization challenges further impede access to ESI, particularly in inpatient and intensive care settings.Approach.To address this challenge, we present a portable and affordable electrode digitization method using the 3D scanning feature in modern iPhone models. This technique combines iPhone scanning with semi-automated image processing using point-cloud electrode selection (PC-ES), a custom MATLAB desktop application. We compare iPhone electrode localization to state-of-the-art photogrammetry technology in a human study with over 6000 electrodes labeled using each method. We also characterize the performance of PC-ES with respect to head location and examine the relative impact of different algorithm parameters.Main Results.The median electrode position variation across reviewers was 1.50 mm for PC-ES scanning and 0.53 mm for photogrammetry, and the average median distance between PC-ES and photogrammetry electrodes was 3.4 mm. These metrics demonstrate comparable performance of iPhone/PC-ES scanning to currently available technology and sufficient accuracy for ESI.Significance.Low cost, portable electrode localization using iPhone scanning removes barriers to ESI in inpatient, outpatient, and remote care settings. While PC-ES has current limitations in user bias and processing time, we anticipate these will improve with software automation techniques as well as future developments in iPhone 3D scanning technology.
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Affiliation(s)
- Alicia Everitt
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Haley Richards
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Yinchen Song
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, United States of America
- Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States of America
| | - Joel Smith
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Erik Kobylarz
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, United States of America
- Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States of America
| | - Timothy Lukovits
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, United States of America
| | - Ryan Halter
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
- Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States of America
| | - Ethan Murphy
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
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Horrillo-Maysonnial A, Avigdor T, Abdallah C, Mansilla D, Thomas J, von Ellenrieder N, Royer J, Bernhardt B, Grova C, Gotman J, Frauscher B. Targeted density electrode placement achieves high concordance with traditional high-density EEG for electrical source imaging in epilepsy. Clin Neurophysiol 2023; 156:262-271. [PMID: 37704552 DOI: 10.1016/j.clinph.2023.08.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/27/2023] [Accepted: 08/12/2023] [Indexed: 09/15/2023]
Abstract
OBJECTIVE High-density (HD) electroencephalography (EEG) is increasingly used in presurgical epilepsy evaluation, but it is demanding in time and resources. To overcome these issues, we compared EEG source imaging (ESI) solutions with a targeted density and HD-EEG montage. METHODS HD-EEGs from patients undergoing presurgical evaluation were analyzed. A low-density recording was created by selecting the 25 electrodes of a standard montage from the 83 electrodes of the HD-EEG and adding 8-11 electrodes around the electrode with the highest amplitude interictal epileptiform discharges. The ESI solution from this "targeted" montage was compared to that from the HD-EEG using the distance between peak vertices, sublobar concordance and a qualitative similarity measure. RESULTS Fifty-eight foci of forty-three patients were included. The median distance between the peak vertices of the two montages was 13.2 mm, irrespective of focus' location. Tangential generators (n = 5/58) showed a higher distance than radial generators (p = 0.04). We found sublobar concordance in 54/58 of the foci (93%). Map similarity, assessed by an epileptologist, had a median score of 4/5. CONCLUSIONS ESI solutions obtained from a targeted density montage show high concordance with those calculated from HD-EEG. SIGNIFICANCE Requiring significantly fewer electrodes, targeted density EEG allows obtaining similar ESI solutions as traditional HD-EEG montage.
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Affiliation(s)
- A Horrillo-Maysonnial
- Clinical Neurophysiology Section, Clínica Universidad de Navarra, Pamplona, Spain; IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain; Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - T Avigdor
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Canada.
| | - C Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Canada.
| | - D Mansilla
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - J Thomas
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - N von Ellenrieder
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - J Royer
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - B Bernhardt
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - C Grova
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Canada; Multimodal Functional Imaging Lab, PERFORM Center, Department of Physics, Concordia University, Montreal, QC, Canada.
| | - J Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Department of Neurology, Duke University Medical Center, Durham, NC, United States; Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, United States.
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10
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Greenblatt AS, Beniczky S, Nascimento FA. Pitfalls in scalp EEG: Current obstacles and future directions. Epilepsy Behav 2023; 149:109500. [PMID: 37931388 DOI: 10.1016/j.yebeh.2023.109500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023]
Abstract
Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.
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Affiliation(s)
- Adam S Greenblatt
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund and Aarhus University Hospital, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Fábio A Nascimento
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
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11
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Tokimoto S, Tokimoto N. Time course of effective connectivity associated with perspective taking in utterance comprehension. Front Hum Neurosci 2023; 17:1179230. [PMID: 38021233 PMCID: PMC10658713 DOI: 10.3389/fnhum.2023.1179230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
This study discusses the effective connectivity in the brain and its time course in realizing perspective taking in verbal communication through electroencephalogram (EEG) associated with the understanding of Japanese utterances. We manipulated perspective taking in a sentence with the Japanese subsidiary verbs -ageru and -kureru, which mean "to give". We measured the EEG during the auditory presentation of the sentences with a multichannel electroencephalograph, and the partial directed coherence and its temporal variations were analyzed using the source localization method to examine causal interactions between nineteen regions of interest in the brain. Three different processing stages were recognized on the basis of the connectivity hubs, direction of information flow, increase or decrease in flow, and temporal variation. We suggest that perspective taking in speech comprehension is realized by interactions between the mentalizing network, mirror neuron network, and executive control network. Furthermore, we found that individual differences in the sociality of typically developing adult speakers were systematically related to effective connectivity. In particular, attention switching was deeply concerned with perspective taking in real time, and the precuneus played a crucial role in implementing individual differences.
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Affiliation(s)
- Shingo Tokimoto
- Department of English Language Studies, Mejiro University, Tokyo, Japan
| | - Naoko Tokimoto
- Department of Performing Arts, Shobi University, Saitama, Japan
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12
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Paramento M, Rubega M, Di Marco R, Contessa P, Agostini M, Cantele F, Masiero S, Formaggio E. Experimental protocol to investigate cortical, muscular and body representation alterations in adolescents with idiopathic scoliosis. PLoS One 2023; 18:e0292864. [PMID: 37824513 PMCID: PMC10569634 DOI: 10.1371/journal.pone.0292864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Adolescent idiopathic scoliosis (AIS) is the most common form of scoliosis. AIS is a three-dimensional morphological spinal deformity that affects approximately 1-3% of adolescents. Not all factors related to the etiology of AIS have yet been identified. OBJECTIVE The primary aim of this experimental protocol is to quantitatively investigate alterations in body representation in AIS, and to quantitatively and objectively track the changes in body sensorimotor representation due to treatment. METHODS Adolescent girls with a confirmed diagnosis of mild (Cobb angle: 10°-20°) or moderate (21°-35°) scoliosis as well as age and sex-matched controls will be recruited. Participants will be asked to perform a 6-min upright standing and two tasks-named target reaching and forearm bisection task. Eventually, subjects will fill in a self-report questionnaire and a computer-based test to assess body image. This evaluation will be repeated after 6 and 12 months of treatment (i.e., partial or full-time brace and physiotherapy corrective postural exercises). RESULTS We expect that theta brain rhythm in the central brain areas, alpha brain rhythm lateralization and body representation will change over time depending on treatment and scoliosis progression as a compensatory strategy to overcome a sensorimotor dysfunction. We also expect asymmetric activation of the trunk muscle during reaching tasks and decreased postural stability in AIS. CONCLUSIONS Quantitatively assess the body representation at different time points during AIS treatment may provide new insights on the pathophysiology and etiology of scoliosis.
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Affiliation(s)
- Matilde Paramento
- Department of Neurosciences, Section of Rehabilitation, University of Padova, Padova, Italy
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Maria Rubega
- Department of Neurosciences, Section of Rehabilitation, University of Padova, Padova, Italy
| | - Roberto Di Marco
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
| | - Paola Contessa
- Orthopedic Rehabilitation Unit, Padova University Hospital, Padova, Italy
| | - Michela Agostini
- Department of Neurosciences, Section of Rehabilitation, University of Padova, Padova, Italy
| | - Francesca Cantele
- Department of Neurosciences, Section of Rehabilitation, University of Padova, Padova, Italy
| | - Stefano Masiero
- Department of Neurosciences, Section of Rehabilitation, University of Padova, Padova, Italy
- Orthopedic Rehabilitation Unit, Padova University Hospital, Padova, Italy
- Ospedale Riabilitativo di Alta Specializzazione di Motta di Livenza, Motta di Livenza, Treviso, Italy
| | - Emanuela Formaggio
- Department of Neurosciences, Section of Rehabilitation, University of Padova, Padova, Italy
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13
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Liu K, Wang Z, Yu Z, Xiao B, Yu H, Wu W. WRA-MTSI: A Robust Extended Source Imaging Algorithm Based on Multi-Trial EEG. IEEE Trans Biomed Eng 2023; 70:2809-2821. [PMID: 37027281 DOI: 10.1109/tbme.2023.3265376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
OBJECTIVE Reconstructing brain activities from electroencephalography (EEG) signals is crucial for studying brain functions and their abnormalities. However, since EEG signals are nonstationary and vulnerable to noise, brain activities reconstructed from single-trial EEG data are often unstable, and significant variability may occur across different EEG trials even for the same cognitive task. METHODS In an effort to leverage the shared information across the EEG data of multiple trials, this paper proposes a multi-trial EEG source imaging method based on Wasserstein regularization, termed WRA-MTSI. In WRA-MTSI, Wasserstein regularization is employed to perform multi-trial source distribution similarity learning, and the structured sparsity constraint is enforced to enable accurate estimation of the source extents, locations and time series. The resulting optimization problem is solved by a computationally efficient algorithm based on the alternating direction method of multipliers (ADMM). RESULTS Both numerical simulations and real EEG data analysis demonstrate that WRA-MTSI outperforms existing single-trial ESI methods (e.g., wMNE, LORETA, SISSY, and SBL) in mitigating the influence of artifacts in EEG data. Moreover, WRA-MTSI yields superior performance compared to other state-of-the-art multi-trial ESI methods (e.g., group lasso, the dirty model, and MTW) in estimating source extents. CONCLUSION AND SIGNIFICANCE WRA-MTSI may serve as an effective robust EEG source imaging method in the presence of multi-trial noisy EEG data.
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14
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Cao J, Bulger E, Shinn-Cunningham B, Grover P, Kainerstorfer JM. Diffuse Optical Tomography Spatial Prior for EEG Source Localization in Human Visual Cortex. Neuroimage 2023:120210. [PMID: 37311535 DOI: 10.1016/j.neuroimage.2023.120210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 05/30/2023] [Indexed: 06/15/2023] Open
Abstract
Electroencephalography (EEG) and diffuse optical tomography (DOT) are imaging methods which are widely used for neuroimaging. While the temporal resolution of EEG is high, the spatial resolution is typically limited. DOT, on the other hand, has high spatial resolution, but the temporal resolution is inherently limited by the slow hemodynamics it measures. In our previous work, we showed using computer simulations that when using the results of DOT reconstruction as the spatial prior for EEG source reconstruction, high spatio-temporal resolution could be achieved. In this work, we experimentally validate the algorithm by alternatingly flashing two visual stimuli at a speed that is faster than the temporal resolution of DOT. We show that the joint reconstruction using both EEG and DOT clearly resolves the two stimuli temporally, and the spatial confinement is drastically improved in comparison to reconstruction using EEG alone.
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Affiliation(s)
- Jiaming Cao
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Eli Bulger
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Barbara Shinn-Cunningham
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Pulkit Grover
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Jana M Kainerstorfer
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States.
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15
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Pascarella A, Mikulan E, Sciacchitano F, Sarasso S, Rubino A, Sartori I, Cardinale F, Zauli F, Avanzini P, Nobili L, Pigorini A, Sorrentino A. An in-vivo validation of ESI methods with focal sources. Neuroimage 2023:120219. [PMID: 37307867 DOI: 10.1016/j.neuroimage.2023.120219] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 05/05/2023] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
Electrophysiological source imaging (ESI) aims at reconstructing the precise origin of brain activity from measurements of the electric field on the scalp. Across laboratories/research centers/hospitals, ESI is performed with different methods, partly due to the ill-posedness of the underlying mathematical problem. However, it is difficult to find systematic comparisons involving a wide variety of methods. Further, existing comparisons rarely take into account the variability of the results with respect to the input parameters. Finally, comparisons are typically performed using either synthetic data, or in-vivo data where the ground-truth is only roughly known. We use an in-vivo high-density EEG dataset recorded during intracranial single pulse electrical stimulation, in which the true sources are substantially dipolar and their locations are precisely known. We compare ten different ESI methods, using their implementation in the MNE-Python package: MNE, dSPM, LORETA, sLORETA, eLORETA, LCMV beamformers, irMxNE, Gamma Map, SESAME and dipole fitting. We perform comparisons under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of such parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with most accurate methods hitting an average localization error of 1.2 cm and outperforming least accurate ones erring by 2.5 cm. As expected, dipolar and sparsity-promoting methods tend to outperform distributed methods. For several distributed methods, the best regularization parameter turned out to be the one in principle associated with low SNR, despite the high SNR of the available dataset. Depth weighting played no role for two out of the six methods implementing it. Sensitivity to input parameters varied widely between methods. While one would expect high variability being associated with low localization error at the best solution, this is not always the case, with some methods producing highly variable results and high localization error, and other methods producing stable results with low localization error. In particular, recent dipolar and sparsity-promoting methods provide significantly better results than older distributed methods. As we repeated the tests with "conventional" (32 channels) and dense (64, 128, 256 channels) EEG recordings, we observed little impact of the number of channels on localization accuracy; however, for distributed methods denser montages provide smaller spatial dispersion. Overall findings confirm that EEG is a reliable technique for localization of point sources and therefore reinforce the importance that ESI may have in the clinical context, especially when applied to identify the surgical target in potential candidates for epilepsy surgery.
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Affiliation(s)
| | - Ezequiel Mikulan
- Department of Biomedical and Clinical Sciences "L. Sacco",Università degli Studi di Milano, Milan, Italy
| | | | - Simone Sarasso
- Department of Biomedical and Clinical Sciences "L. Sacco",Università degli Studi di Milano, Milan, Italy
| | - Annalisa Rubino
- Department of Neurosciences, Center for Epilepsy Surgery "C. Munari", Hospital Niguarda, Milan, Italy
| | - Ivana Sartori
- Department of Neurosciences, Center for Epilepsy Surgery "C. Munari", Hospital Niguarda, Milan, Italy
| | - Francesco Cardinale
- Department of Neurosciences, Center for Epilepsy Surgery "C. Munari", Hospital Niguarda, Milan, Italy
| | - Flavia Zauli
- Department of Biomedical and Clinical Sciences "L. Sacco",Università degli Studi di Milano, Milan, Italy
| | | | - Lino Nobili
- Child Neuropsychiatry Unit, IRCCS "G. Gaslini" Institute, Genoa, Italy; DINOGMI, Università degli Studi di Genova, Genoa, Italy
| | - Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, Italy
| | - Alberto Sorrentino
- Department of Mathematics, Università degli Studi di Genova, Genoa, Italy.
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16
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Mentzelopoulos G, Driscoll N, Shankar S, Kim B, Rich R, Fernandez-Nunez G, Stoll H, Erickson B, Medaglia JD, Vitale F. Alerting attention is sufficient to induce a phase-dependent behavior that can be predicted by frontal EEG. Front Behav Neurosci 2023; 17:1176865. [PMID: 37292166 PMCID: PMC10246752 DOI: 10.3389/fnbeh.2023.1176865] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/27/2023] [Indexed: 06/10/2023] Open
Abstract
Recent studies suggest that attention is rhythmic. Whether that rhythmicity can be explained by the phase of ongoing neural oscillations, however, is still debated. We contemplate that a step toward untangling the relationship between attention and phase stems from employing simple behavioral tasks that isolate attention from other cognitive functions (perception/decision-making) and by localized monitoring of neural activity with high spatiotemporal resolution over the brain regions associated with the attentional network. In this study, we investigated whether the phase of electroencephalography (EEG) oscillations predicts alerting attention. We isolated the alerting mechanism of attention using the Psychomotor Vigilance Task, which does not involve a perceptual component, and collected high resolution EEG using novel high-density dry EEG arrays at the frontal region of the scalp. We identified that alerting attention alone is sufficient to induce a phase-dependent modulation of behavior at EEG frequencies of 3, 6, and 8 Hz throughout the frontal region, and we quantified the phase that predicts the high and low attention states in our cohort. Our findings disambiguate the relationship between EEG phase and alerting attention.
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Affiliation(s)
- Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, United States
| | - Nicolette Driscoll
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, United States
| | - Sneha Shankar
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, United States
| | - Brian Kim
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
| | - Ryan Rich
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
| | | | - Harrison Stoll
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
| | - Brian Erickson
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
| | - John Dominic Medaglia
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, United States
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Drexel University, Philadelphia, PA, United States
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, United States
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA, United States
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17
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Frauscher B, Bénar CG, Engel JJ, Grova C, Jacobs J, Kahane P, Wiebe S, Zjilmans M, Dubeau F. Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy. Epilepsy Behav 2023; 143:109221. [PMID: 37119580 DOI: 10.1016/j.yebeh.2023.109221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.
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Affiliation(s)
- B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - J Jr Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - C Grova
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, QC, Canada; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| | - J Jacobs
- Department of Pediatric and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - P Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institute Neurosciences, Department of Neurology, 38000 Grenoble, France
| | - S Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Zjilmans
- Stichting Epilepsie Instellingen Nederland, The Netherlands; Brain Center, University Medical Center Utrecht, The Netherlands
| | - F Dubeau
- Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
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18
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Jiang L, Li F, Chen Z, Zhu B, Yi C, Li Y, Zhang T, Peng Y, Si Y, Cao Z, Chen A, Yao D, Chen X, Xu P. Information transmission velocity-based dynamic hierarchical brain networks. Neuroimage 2023; 270:119997. [PMID: 36868393 DOI: 10.1016/j.neuroimage.2023.119997] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 02/09/2023] [Accepted: 02/27/2023] [Indexed: 03/05/2023] Open
Abstract
The brain functions as an accurate circuit that regulates information to be sequentially propagated and processed in a hierarchical manner. However, it is still unknown how the brain is hierarchically organized and how information is dynamically propagated during high-level cognition. In this study, we developed a new scheme for quantifying the information transmission velocity (ITV) by combining electroencephalogram (EEG) and diffusion tensor imaging (DTI), and then mapped the cortical ITV network (ITVN) to explore the information transmission mechanism of the human brain. The application in MRI-EEG data of P300 revealed bottom-up and top-down ITVN interactions subserving P300 generation, which was comprised of four hierarchical modules. Among these four modules, information exchange between visual- and attention-activated regions occurred at a high velocity, related cognitive processes could thus be efficiently accomplished due to the heavy myelination of these regions. Moreover, inter-individual variability in P300 was probed to be attributed to the difference in information transmission efficiency of the brain, which may provide new insight into the cognitive degenerations in clinical neurodegenerative disorders, such as Alzheimer's disease, from the transmission velocity perspective. Together, these findings confirm the capacity of ITV to effectively determine the efficiency of information propagation in the brain.
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Affiliation(s)
- Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zhaojin Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bin Zhu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Tao Zhang
- School of science, Xihua University, Chengdu 610039, China
| | - Yueheng Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yajing Si
- School of Psychology, Xinxiang Medical University, Xinxiang 453003, China
| | - Zehong Cao
- STEM, University of South Australia, Adelaide, SA 5000, Australia
| | - Antao Chen
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China.
| | - Xun Chen
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China; Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230026, China.
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China.
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19
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Miraglia F, Pappalettera C, Di Ienno S, Nucci L, Cacciotti A, Manenti R, Judica E, Rossini PM, Vecchio F. The Effects of Directional and Non-Directional Stimuli during a Visuomotor Task and Their Correlation with Reaction Time: An ERP Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:3143. [PMID: 36991853 PMCID: PMC10058543 DOI: 10.3390/s23063143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
Different visual stimuli can capture and shift attention into different directions. Few studies have explored differences in brain response due to directional (DS) and non-directional visual stimuli (nDS). To explore the latter, event-related potentials (ERP) and contingent negative variation (CNV) during a visuomotor task were evaluated in 19 adults. To examine the relation between task performance and ERPs, the participants were divided into faster (F) and slower (S) groups based on their reaction times (RTs). Moreover, to reveal ERP modulation within the same subject, each recording from the single participants was subdivided into F and S trials based on the specific RT. ERP latencies were analysed between conditions ((DS, nDS); (F, S subjects); (F, S trials)). Correlation was analysed between CNV and RTs. Our results reveal that the ERPs' late components are modulated differently by DS and nDS conditions in terms of amplitude and location. Differences in ERP amplitude, location and latency, were also found according to subjects' performance, i.e., between F and S subjects and trials. In addition, results show that the CNV slope is modulated by the directionality of the stimulus and contributes to motor performance. A better understanding of brain dynamics through ERPs could be useful to explain brain states in healthy subjects and to support diagnoses and personalized rehabilitation in patients with neurological diseases.
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Affiliation(s)
- Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
| | - Sara Di Ienno
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
| | - Lorenzo Nucci
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
| | - Alessia Cacciotti
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
| | - Rosa Manenti
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di DioFatebenefratelli, 25125 Brescia, Italy
| | - Elda Judica
- Casa di Cura IGEA, Department of Neurorehabilitation Sciences, 20144 Milano, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
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20
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Samudra N, Ranasinghe K, Kirsch H, Rankin K, Miller B. Etiology and Clinical Significance of Network Hyperexcitability in Alzheimer's Disease: Unanswered Questions and Next Steps. J Alzheimers Dis 2023; 92:13-27. [PMID: 36710680 DOI: 10.3233/jad-220983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Cortical network hyperexcitability related to synaptic dysfunction in Alzheimer's disease (AD) is a potential target for therapeutic intervention. In recent years, there has been increased interest in the prevalence of silent seizures and interictal epileptiform discharges (IEDs, or seizure tendency), with both entities collectively termed "subclinical epileptiform activity" (SEA), on neurophysiologic studies in AD patients. SEA has been demonstrated to be common in AD, with prevalence estimates ranging between 22-54%. Converging lines of basic and clinical evidence imply that modifying a hyperexcitable state results in an improvement in cognition. In particular, though these results require further confirmation, post-hoc findings from a recent phase II clinical trial suggest a therapeutic effect with levetiracetam administration in patients with AD and IEDs. Here, we review key unanswered questions as well as potential clinical trial avenues. Specifically, we discuss postulated mechanisms and treatment of hyperexcitability in patients with AD, which are of interest in designing future disease-modifying therapies. Criteria to prompt screening and optimal screening methodology for hyperexcitability have yet to be defined, as does timing and personalization of therapeutic intervention.
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Affiliation(s)
- Niyatee Samudra
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Kamalini Ranasinghe
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Heidi Kirsch
- University of California, San Francisco Comprehensive Epilepsy Center, San Francisco, CA, USA
| | - Katherine Rankin
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
| | - Bruce Miller
- Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA
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21
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Bo J, Acluche F, Lasutschinkow PC, Augustiniak A, Ditchfield N, Lajiness-O'Neill R. Motor networks in children with autism spectrum disorder: a systematic review on EEG studies. Exp Brain Res 2022; 240:3073-3087. [PMID: 36260095 DOI: 10.1007/s00221-022-06483-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 10/09/2022] [Indexed: 01/15/2023]
Abstract
Motor disturbance and altered motor networks are commonly reported in individuals with autism spectrum disorder (ASD). It has been suggested that electroencephalogram (EEG) can be used to provide exquisite temporal resolution for understanding motor control processes in ASD. However, the variability of study design and EEG approaches can impact our interpretation. Here, we conducted a systematic review on recent 11 EEG studies that involve motor observation and/or execution tasks and evaluated how these findings help us understand motor difficulties in ASD. Three behavior paradigms with different EEG analytic methods were demonstrated. The main findings were quite mixed: children with ASD did not always show disrupted neuronal activity during motor observation. Additionally, they might have intact ability for movement execution but have more difficulties in neuronal modulation during movement preparation. We would like to promote discussions on how methodological selections of behavioral tasks and data analytic approaches impact our interpretation of motor deficits in ASD. Future EEG research addressing the inconsistency across methodological approaches is necessary to help us understand neurophysiological mechanism of motor abnormalities in ASD.
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Affiliation(s)
- Jin Bo
- Department of Psychology, Eastern Michigan University, 341 MJ Science Building, Ypsilanti, MI, 48197, USA. .,Neuroscience Program, Eastern Michigan University, 341 MJ Science Building, Ypsilanti, MI, 48197, USA.
| | - Frantzy Acluche
- Department of Psychology, Eastern Michigan University, 341 MJ Science Building, Ypsilanti, MI, 48197, USA
| | - Patricia C Lasutschinkow
- Department of Psychology, Eastern Michigan University, 341 MJ Science Building, Ypsilanti, MI, 48197, USA
| | - Alyssa Augustiniak
- Department of Psychology, Eastern Michigan University, 341 MJ Science Building, Ypsilanti, MI, 48197, USA
| | - Noelle Ditchfield
- Department of Psychology, Eastern Michigan University, 341 MJ Science Building, Ypsilanti, MI, 48197, USA
| | - Renee Lajiness-O'Neill
- Department of Psychology, Eastern Michigan University, 341 MJ Science Building, Ypsilanti, MI, 48197, USA.,Neuroscience Program, Eastern Michigan University, 341 MJ Science Building, Ypsilanti, MI, 48197, USA
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22
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Miller KJ, Fine AL. Decision-making in stereotactic epilepsy surgery. Epilepsia 2022; 63:2782-2801. [PMID: 35908245 PMCID: PMC9669234 DOI: 10.1111/epi.17381] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 11/27/2022]
Abstract
Surgery can cure or significantly improve both the frequency and the intensity of seizures in patients with medication-refractory epilepsy. The set of diagnostic and therapeutic interventions involved in the path from initial consultation to definitive surgery is complex and includes a multidisciplinary team of neurologists, neurosurgeons, neuroradiologists, and neuropsychologists, supported by a very large epilepsy-dedicated clinical architecture. In recent years, new practices and technologies have emerged that dramatically expand the scope of interventions performed. Stereoelectroencephalography has become widely adopted for seizure localization; stereotactic laser ablation has enabled more focal, less invasive, and less destructive interventions; and new brain stimulation devices have unlocked treatment of eloquent foci and multifocal onset etiologies. This article articulates and illustrates the full framework for how epilepsy patients are considered for surgical intervention, with particular attention given to stereotactic approaches.
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Affiliation(s)
- Kai J. Miller
- Neurosurgery, Mayo Clinic, 200 First St., Rochester, MN, 55902
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23
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Levakova M, Christensen JH, Ditlevsen S. Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220621. [PMID: 36465674 PMCID: PMC9709569 DOI: 10.1098/rsos.220621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 11/08/2022] [Indexed: 06/17/2023]
Abstract
Electroencephalogram (EEG) is a popular tool for studying brain activity. Numerous statistical techniques exist to enhance understanding of the complex dynamics underlying the EEG recordings. Inferring the functional network connectivity between EEG channels is of interest, and non-parametric inference methods are typically applied. We propose a fully parametric model-based approach via cointegration analysis. It not only estimates the network but also provides further insight through cointegration vectors, which characterize equilibrium states, and the corresponding loadings, which describe the mechanism of how the EEG dynamics is drawn to the equilibrium. We outline the estimation procedure in the context of EEG data, which faces specific challenges compared with the common econometric problems, for which cointegration analysis was originally conceived. In particular, the dimension is higher, typically around 64; there is usually access to repeated trials; and the data are artificially linearly dependent through the normalization done in EEG recordings. Finally, we illustrate the method on EEG data from a visual task experiment and show how brain states identified via cointegration analysis can be utilized in further investigations of determinants playing roles in sensory identifications.
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Affiliation(s)
- Marie Levakova
- Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Ø, Denmark
| | | | - Susanne Ditlevsen
- Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Ø, Denmark
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24
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Perceptual Awareness and Its Relationship with Consciousness: Hints from Perceptual Multistability. NEUROSCI 2022. [DOI: 10.3390/neurosci3040039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Many interesting theories of consciousness have been proposed, but so far, there is no “unified” theory capable of encompassing all aspects of this phenomenon. We are all aware of what it feels like to be conscious and what happens if there is an absence of consciousness. We are becoming more and more skilled in measuring consciousness states; nevertheless, we still “don’t get it” in its deeper essence. How does all the processed information converge from different brain areas and structures to a common unity, giving us this very private “feeling of being conscious”, despite the constantly changing flow of information between internal and external states? “Multistability” refers to a class of perceptual phenomena where subjective awareness spontaneously and continuously alternates between different percepts, although the objective stimuli do not change, supporting the idea that the brain “interprets” sensorial input in a “constructive” way. In this perspective paper, multistability and perceptual awareness are discussed as a methodological window for understanding the “local” states of consciousness, a privileged position from which it is possible to observe the brain dynamics and mechanisms producing the subjective phenomena of perceptual awareness in the very moment they are happening.
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25
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Das A, Mandel A, Shitara H, Popa T, Horovitz SG, Hallett M, Thirugnanasambandam N. Evaluating interhemispheric connectivity during midline object recognition using EEG. PLoS One 2022; 17:e0270949. [PMID: 36026515 PMCID: PMC9417031 DOI: 10.1371/journal.pone.0270949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 06/22/2022] [Indexed: 11/20/2022] Open
Abstract
Functional integration between two hemispheres is crucial for perceptual binding to occur when visual stimuli are presented in the midline of the visual field. Mima and colleagues (2001) showed using EEG that midline object recognition was associated with task-related decrease in alpha band power (alpha desynchronisation) and a transient increase in interhemispheric coherence. Our objective in the current study was to replicate the results of Mima et al. and to further evaluate interhemispheric effective connectivity during midline object recognition in source space. We recruited 11 healthy adult volunteers and recorded EEG from 64 channels while they performed a midline object recognition task. Task-related power and coherence were estimated in sensor and source spaces. Further, effective connectivity was evaluated using Granger causality. While we were able to replicate the alpha desynchronisation associated with midline object recognition, we could not replicate the coherence results of Mima et al. The data-driven approach that we employed in our study localised the source of alpha desynchronisation over the left occipito-temporal region. In the alpha band, we further observed significant increase in imaginary part of coherency between bilateral occipito-temporal regions during object recognition. Finally, Granger causality analysis between the left and right occipito-temporal regions provided an insight that even though there is bidirectional interaction, the left occipito-temporal region may be crucial for integrating the information necessary for object recognition. The significance of the current study lies in using high-density EEG and applying more appropriate and robust measures of connectivity as well as statistical analysis to validate and enhance our current knowledge on the neural basis of midline object recognition.
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Affiliation(s)
- Anwesha Das
- Human Motor Neurophysiology and Neuromodulation Lab, National Brain Research Centre (NBRC), Manesar, Haryana, India
| | - Alexandra Mandel
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, Maryland, United States of America
- The George Washington University, Washington, DC, United States of America
| | - Hitoshi Shitara
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, Maryland, United States of America
- Department of Orthopaedic Surgery, Gunma University Graduate School of Medicine, Tokyo, Japan
| | - Traian Popa
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, Maryland, United States of America
| | - Silvina G. Horovitz
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, Maryland, United States of America
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, Maryland, United States of America
| | - Nivethida Thirugnanasambandam
- Human Motor Neurophysiology and Neuromodulation Lab, National Brain Research Centre (NBRC), Manesar, Haryana, India
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, Maryland, United States of America
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26
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Li W, Zhang W, Jiang Z, Zhou T, Xu S, Zou L. Source localization and functional network analysis in emotion cognitive reappraisal with EEG-fMRI integration. Front Hum Neurosci 2022; 16:960784. [PMID: 36034109 PMCID: PMC9411793 DOI: 10.3389/fnhum.2022.960784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundThe neural activity and functional networks of emotion-based cognitive reappraisal have been widely investigated using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). However, single-mode neuroimaging techniques are limited in exploring the regulation process with high temporal and spatial resolution.ObjectivesWe proposed a source localization method with multimodal integration of EEG and fMRI and tested it in the source-level functional network analysis of emotion cognitive reappraisal.MethodsEEG and fMRI data were simultaneously recorded when 15 subjects were performing the emotional cognitive reappraisal task. Fused priori weighted minimum norm estimation (FWMNE) with sliding windows was proposed to trace the dynamics of EEG source activities, and the phase lag index (PLI) was used to construct the functional brain network associated with the process of downregulating negative affect using the reappraisal strategy.ResultsThe functional networks were constructed with the measure of PLI, in which the important regions were indicated. In the gamma band source-level network analysis, the cuneus, the lateral orbitofrontal cortex, the superior parietal cortex, the postcentral gyrus, and the pars opercularis were identified as important regions in reappraisal with high betweenness centrality.ConclusionThe proposed multimodal integration method for source localization identified the key cortices involved in emotion regulation, and the network analysis demonstrated the important brain regions involved in the cognitive control of reappraisal. It shows promise in the utility in the clinical setting for affective disorders.
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Affiliation(s)
- Wenjie Li
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Wei Zhang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
| | - Zhongyi Jiang
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Tiantong Zhou
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
| | - Shoukun Xu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
- *Correspondence: Shoukun Xu
| | - Ling Zou
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province, Hangzhou, China
- Ling Zou
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27
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Triccas LT, Camilleri KP, Tracey C, Mansoureh FH, Benjamin W, Francesca M, Leonardo B, Dante M, Geert V. Reliability of Upper Limb Pin-Prick Stimulation With Electroencephalography: Evoked Potentials, Spectra and Source Localization. Front Hum Neurosci 2022; 16:881291. [PMID: 35937675 PMCID: PMC9351050 DOI: 10.3389/fnhum.2022.881291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
In order for electroencephalography (EEG) with sensory stimuli measures to be used in research and neurological clinical practice, demonstration of reliability is needed. However, this is rarely examined. Here we studied the test-retest reliability of the EEG latency and amplitude of evoked potentials and spectra as well as identifying the sources during pin-prick stimulation. We recorded EEG in 23 healthy older adults who underwent a protocol of pin-prick stimulation on the dominant and non-dominant hand. EEG was recorded in a second session with rest intervals of 1 week. For EEG electrodes Fz, Cz, and Pz peak amplitude, latency and frequency spectra for pin-prick evoked potentials was determined and test-retest reliability was assessed. Substantial reliability ICC scores (0.76-0.79) were identified for evoked potential negative-positive amplitude from the left hand at C4 channel and positive peak latency when stimulating the right hand at Cz channel. Frequency spectra showed consistent increase of low-frequency band activity (< 5 Hz) and also in theta and alpha bands in first 0.25 s. Almost perfect reliability scores were found for activity at both low-frequency and theta bands (ICC scores: 0.81-0.98). Sources were identified in the primary somatosensory and motor cortices in relation to the positive peak using s-LORETA analysis. Measuring the frequency response from the pin-prick evoked potentials may allow the reliable assessment of central somatosensory impairment in the clinical setting.
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Affiliation(s)
- Lisa Tedesco Triccas
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Systems and Control Engineering, University of Malta, Msida, Malta
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Hasselt, Belgium
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta
| | - Kenneth P. Camilleri
- Department of Systems and Control Engineering, University of Malta, Msida, Malta
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta
| | - Camilleri Tracey
- Department of Systems and Control Engineering, University of Malta, Msida, Malta
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta
| | - Fahimi Hnazaee Mansoureh
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
- The Wellcome Trust Centre for Neuroimaging, University College London Institute of Neurology, London, United Kingdom
| | | | - Muscat Francesca
- Department of Systems and Control Engineering, University of Malta, Msida, Malta
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta
| | - Boccuni Leonardo
- Institut Guttmann, Institut Universitari de Neurorehabilitació Adscrit a la Universitat Autónoma de Barcelona, Barcelona, Spain
- Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
- Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Barcelona, Spain
| | - Mantini Dante
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Verheyden Geert
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
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28
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Holroyd CB. Interbrain synchrony: on wavy ground. Trends Neurosci 2022; 45:346-357. [PMID: 35236639 DOI: 10.1016/j.tins.2022.02.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 01/08/2022] [Accepted: 02/04/2022] [Indexed: 12/15/2022]
Abstract
In recent years the study of dynamic, between-brain coupling mechanisms has taken social neuroscience by storm. In particular, interbrain synchrony (IBS) is a putative neural mechanism said to promote social interactions by enabling the functional integration of multiple brains. In this article, I argue that this research is beset with three pervasive and interrelated problems. First, the field lacks a widely accepted definition of IBS. Second, IBS wants for theories that can guide the design and interpretation of experiments. Third, a potpourri of tasks and empirical methods permits undue flexibility when testing the hypothesis. These factors synergistically undermine IBS as a theoretical construct. I finish by recommending measures that can address these issues.
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Affiliation(s)
- Clay B Holroyd
- Department of Experimental Psychology, Ghent University, Henri Dunantlaan 2, 9000 Gent, Belgium.
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29
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Bosch-Bayard J, Biscay RJ, Fernandez T, Otero GA, Ricardo-Garcell J, Aubert-Vazquez E, Evans AC, Harmony T. EEG effective connectivity during the first year of life mirrors brain synaptogenesis, myelination, and early right hemisphere predominance. Neuroimage 2022; 252:119035. [PMID: 35218932 DOI: 10.1016/j.neuroimage.2022.119035] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/25/2021] [Accepted: 02/22/2022] [Indexed: 10/19/2022] Open
Abstract
INTRODUCTION The maturation of electroencephalogram (EEG) effective connectivity in healthy infants during the first year of life is described. METHODS Participants: A cross-sectional sample of 125 healthy at-term infants, from 0 to 12 months of age, underwent EEG in a state of quiet sleep. PROCEDURES The EEG primary currents at the source were described with the sLoreta method. An unmixing algorithm was applied to reduce the leakage, and the isolated effective coherence, a direct and directed measurement of information flow, was calculated. RESULTS AND DISCUSSION Initially, the highest indices of connectivity are at the subcortical nuclei, continuing to the parietal lobe, predominantly the right hemisphere, then expanding to temporal, occipital, and finally the frontal areas, which is consistent with the myelination process. Age-related connectivity changes were mostly long-range and bilateral. Connections increased with age, mainly in the right hemisphere, while they mainly decreased in the left hemisphere. Increased connectivity from 20 to 30 Hz, mostly at the right hemisphere. These findings were consistent with right hemisphere predominance during the first three years of life. Theta and alpha connections showed the greatest changes with age. Strong connectivity was found between the parietal, temporal, and occipital regions to the frontal lobes, responsible for executive functions and consistent with behavioral development during the first year. The thalamus exchanges information bidirectionally with all cortical regions and frequency bands. CONCLUSIONS The maturation of EEG connectivity during the first year in healthy infants is very consistent with synaptogenesis, reductions in synaptogenesis, myelination, and functional and behavioral development.
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Affiliation(s)
- Jorge Bosch-Bayard
- McGill Center for Integrative Neuroscience (MCIN), Ludmer Center for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal H3A2B4, Canada
| | - Rolando J Biscay
- Centro de Investigación en Matemáticas, Guanajuato 36023, Mexico
| | - Thalia Fernandez
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico
| | - Gloria A Otero
- Facultad de Medicina, Universidad Autónoma del Estado de México, Toluca de Lerdo 50180, Mexico
| | - Josefina Ricardo-Garcell
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico
| | | | - Alan C Evans
- McGill Center for Integrative Neuroscience (MCIN), Ludmer Center for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal H3A2B4, Canada
| | - Thalia Harmony
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico.
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30
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Cox BC, Danoun OA, Lundstrom BN, Lagerlund TD, Wong-Kisiel LC, Brinkmann BH. EEG source imaging concordance with intracranial EEG and epileptologist review in focal epilepsy. Brain Commun 2021; 3:fcab278. [PMID: 34877536 PMCID: PMC8643498 DOI: 10.1093/braincomms/fcab278] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 12/14/2022] Open
Abstract
EEG source imaging is becoming widely used for the evaluation of medically refractory focal epilepsy. The validity of EEG source imaging has been established in several studies comparing source imaging to the surgical resection cavity and subsequent seizure freedom. We present a cohort of 87 patients and compare EEG source imaging of both ictal and interictal scalp EEG to the seizure onset zone on intracranial EEG. Concordance of EEG source imaging with intracranial EEG was determined on a sublobar level and was quantified by measuring the distance between the source imaging result and the centroid of the active seizure onset zone electrodes. The EEG source imaging results of a subgroup of 26 patients with high density 76-channel EEG were compared with the localization of three experienced epileptologists. Of 87 patients, 95% had at least one analysis concordant with intracranial EEG and 74% had complete concordance. There was a higher rate of complete concordance in temporal lobe epilepsy compared to extratemporal (89.3 and 62.8%, respectively, P = 0.015). Of the total 282 analyses performed on this cohort, higher concordance was also seen in temporal discharges (95%) compared to extratemporal (77%) (P = 0.0012), but no difference was seen comparing high-density EEG with standard (32-channel) EEG. Subgroup analysis of ictal waveforms showed greater concordance for ictal spiking, compared with rhythmic activity, paroxysmal fast activity, or obscured onset. Median distances from the dipole and maximum distributed source to a centroid of seizure onset zone electrodes were 30.0 and 32.5 mm, respectively, and the median distances from dipole and maximum distributed source to nearest seizure onset zone electrode were 22.8 and 21.7, respectively. There were significantly shorter distances in ictal spiking. There were shorter distances in patients with Engel Class 1 outcome from surgical resection compared to patients with worse outcomes. For the subgroup of 26 high-density EEG patients, EEG source localization had a significantly higher concordance (92% versus 65%), sensitivity (57% versus 35%) and positive predictive value (60% versus 36%) compared with epileptologist localization. Our study demonstrates good concordance between ictal and interictal source imaging and intracranial EEG. Temporal lobe discharges have higher concordance rates than extratemporal discharges. Importantly, this study shows that source imaging has greater agreement with intracranial EEG than visual review alone, supporting its role in surgical planning.
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Affiliation(s)
- Benjamin C Cox
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Omar A Danoun
- Department of Neurology, Henry Ford Hospital, Detroit, MI 48202, USA
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31
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Chen RH, Li BF, Wen JH, Zhong CL, Ji MM. Clinical and electroencephalogram characteristics and treatment outcomes in children with benign epilepsy and centrotemporal spikes. World J Clin Cases 2021; 9:10116-10125. [PMID: 34904081 PMCID: PMC8638062 DOI: 10.12998/wjcc.v9.i33.10116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/08/2021] [Accepted: 08/27/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Epilepsy is a syndrome characterized by transient, rigid, paroxysmal, and repetitive central nervous system dysfunction. Prevention, control, and improvement of cognitive and behavioral dysfunction are of great significance for improving the patients’ intellectual development and quality of life. Electroencephalograms (EEG) can predict an accelerated decline in cognitive function.
AIM To determine the clinical and EEG characteristics and treatment results of benign epilepsy in spiking children.
METHODS A total of 106 cases of benign epilepsy in children with myocardial spines treated at our hospital from January 2017 to January 2020 were selected. Differences in clinical data and EGG characteristics between treatment-effective/-ineffective patients were analyzed, and children’s intellectual development before and after treatment evaluated using the Gesell Development Diagnostic Scale.
RESULTS EEG showed that the discharge proportion in the awake and sleep periods was 66.04%, and the peak/peak discharge was mainly single-sided, accounting for 81.13%, while the discharge generalization accounted for 31.13%. There was no significant difference in any of these variables between sexes and ages (P > 0.05). The proportion of patients with early onset (< 5 years old) and seizure frequency > 3 times/half a year was 40.00% and 60.00%, respectively; the incidence rate and seizure frequency in the younger age group (< 5 years old) were significantly higher than those in the treatment-effective group (P < 0.05), while the discharge index was significantly lower than that in the treatment-effective group (P < 0.05). The discharge index was negatively correlated with fine motor skill and language development (r = -0.274 and -0.247, respectively; P < 0.05), but not with the rest (P > 0.05). Logistic regression analysis showed that low age onset (< 5 years old) and seizure frequency were the factors affecting ineffective-treatment of benign epilepsy in children (odds ratio = 11.304 and 5.784, respectively; P < 0.05). The discharge index of the responsive group after treatment was significantly lower than that of the unresponsive group (P < 0.05). However, there was no significant difference between groups after treatment in gross and fine motor skills, adaptability, language, and personal social development (P > 0.05).
CONCLUSION The EEG of children with benign epilepsy due to spinal wave in central time zone has characteristic changes, and the therapeutic effect is influenced by age of onset and attack frequency.
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Affiliation(s)
- Rui-Hua Chen
- Department of Children’s Neurology, Ganzhou Maternal and Child Health Hospital, Ganzhou 341000, Jiangxi Province, China
| | - Bing-Fei Li
- Department of Pediatrics, Ganzhou Maternal and Child Health Hospital, Ganzhou 341000, Jiangxi Province, China
| | - Jian-Hua Wen
- Department of Pediatrics, Ningdu County People's Hospital, Ganzhou 341000, Jiangxi Province, China
| | - Chun-Lan Zhong
- Department of Pediatrics, Ganzhou Maternal and Child Health Hospital, Ganzhou 341000, Jiangxi Province, China
| | - Ming-Ming Ji
- Department of Pediatrics, Ganzhou Maternal and Child Health Hospital, Ganzhou 341000, Jiangxi Province, China
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Thurairajah A, Freibauer A, RamachandranNair R, Whitney R, Jain P, Donner E, Widjaja E, Jones KC. Low density electrical source imaging of the ictal onset zone in the surgical evaluation of children with epilepsy. Epilepsy Res 2021; 178:106810. [PMID: 34784573 DOI: 10.1016/j.eplepsyres.2021.106810] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 10/18/2021] [Accepted: 11/02/2021] [Indexed: 01/14/2023]
Abstract
PURPOSE To investigate the utility of Low Density (LD) Electrical Source Imaging (ESI) to model the ictal onset zone (IOZ) for the surgical work up of children with medically refractory epilepsy. METHODS This was a retrospective review of 12 patients from a district and regional pediatric epilepsy center, who underwent focal resections between 2014 and 2019. ESI was generated using the Curry 8 software, incorporating T1 Magnetic Resonance Imaging (MRI) scans and scalp electroencephalogram (EEG) recordings. Concordance of the ictal LD-ESI localizations to the epileptogenic zone was assessed by comparing the location of the ictal LD-ESI to the focal resection margins on neuroimaging and noting the post-operative outcomes at one year. Localizations determined by ictal LD-ESI were also compared to interictal LD-ESI, positron emission tomography (FDG-PET) and interictal magnetoencephalography (MEG). RESULTS Ictal ESI correctly localized the ictal onset zone in 4/6 patients, with all four being seizure free at one year. Similarly, interictal ESI localized the irritative zone in 7/9 patients with focal resections, with 6/7 being seizure free at one year. Additionally, we observed ictal ESI to be concordant to interictal ESI in 5/6 patients. Ictal ESI and interictal ESI were concordant to interictal MEG in 3/6 patients. Ictal ESI was concordant with FDG-PET in 6/7 cases. CONCLUSION IOZ source localization through LD-ESI is a promising complementary method of assessing the epileptogenic focus in children. These findings may support the inclusion of ictal LD-ESI within the pre-surgical evaluation of children to supplement current diagnostic tools.
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Affiliation(s)
- Arun Thurairajah
- The Division of Neurology, Department of Pediatrics, McMaster Children's Hospital, Hamilton, ON, Canada
| | - Alexander Freibauer
- The Division of Neurology, Department of Pediatrics, McMaster Children's Hospital, Hamilton, ON, Canada
| | - Rajesh RamachandranNair
- The Division of Neurology, Department of Pediatrics, McMaster Children's Hospital, Hamilton, ON, Canada
| | - Robyn Whitney
- The Division of Neurology, Department of Pediatrics, McMaster Children's Hospital, Hamilton, ON, Canada
| | - Puneet Jain
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Elizabeth Donner
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Elysa Widjaja
- The Division of Neuroimaging, Department of Diagnostic Imaging, The Hospital for Sick Children Toronto ON, Canada
| | - Kevin C Jones
- The Division of Neurology, Department of Pediatrics, McMaster Children's Hospital, Hamilton, ON, Canada.
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Briden M, Norouzi N. WaveFusion Squeeze-and-Excitation: Towards an Accurate and Explainable Deep Learning Framework in Neuroscience. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1092-1095. [PMID: 34891477 DOI: 10.1109/embc46164.2021.9630605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We introduce WaveFusion Squeeze-and-Excite, a multi-modal deep fusion architecture, as a practical and effective framework for classifying and localizing neurological events. WaveFusion SE is composed of lightweight CNNs for per-lead time-frequency analysis and an attention network called squeeze and excitation network with a temperature factor for effectively integrating lightweight modalities for final prediction. Our proposed architecture demonstrates high accuracy in classifying subjects' anxiety levels with an overall accuracy of 97.53%, beating prior approaches by a considerable margin. As will also be demonstrated in the paper, our approach allows for real-time localization of neurological events during the inference without any additional post-processing. This is a great step towards an explainable DL framework for neuroscience applications.
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34
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Iannotti GR, Orepic P, Brunet D, Koenig T, Alcoba-Banqueri S, Garin DFA, Schaller K, Blanke O, Michel CM. EEG Spatiotemporal Patterns Underlying Self-other Voice Discrimination. Cereb Cortex 2021; 32:1978-1992. [PMID: 34649280 PMCID: PMC9070353 DOI: 10.1093/cercor/bhab329] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 11/24/2022] Open
Abstract
There is growing evidence showing that the representation of the human “self” recruits special systems across different functions and modalities. Compared to self-face and self-body representations, few studies have investigated neural underpinnings specific to self-voice. Moreover, self-voice stimuli in those studies were consistently presented through air and lacking bone conduction, rendering the sound of self-voice stimuli different to the self-voice heard during natural speech. Here, we combined psychophysics, voice-morphing technology, and high-density EEG in order to identify the spatiotemporal patterns underlying self-other voice discrimination (SOVD) in a population of 26 healthy participants, both with air- and bone-conducted stimuli. We identified a self-voice-specific EEG topographic map occurring around 345 ms post-stimulus and activating a network involving insula, cingulate cortex, and medial temporal lobe structures. Occurrence of this map was modulated both with SOVD task performance and bone conduction. Specifically, the better participants performed at SOVD task, the less frequently they activated this network. In addition, the same network was recruited less frequently with bone conduction, which, accordingly, increased the SOVD task performance. This work could have an important clinical impact. Indeed, it reveals neural correlates of SOVD impairments, believed to account for auditory-verbal hallucinations, a common and highly distressing psychiatric symptom.
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Affiliation(s)
- Giannina Rita Iannotti
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, 1202, Switzerland.,Department of Neurosurgery, University Hospitals of Geneva and Faculty of Medicine, University of Geneva, 1205, Switzerland
| | - Pavo Orepic
- Laboratory of Cognitive Neuroscience, Center for Neuroprosthetics and Brain Mind Institute, Faculty of Life Sciences, Swiss Federal Institute of Technology (EPFL), 1202, Switzerland
| | - Denis Brunet
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, 1202, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne and Geneva, 1015, Switzerland
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern 3000, Switzerland
| | - Sixto Alcoba-Banqueri
- Laboratory of Cognitive Neuroscience, Center for Neuroprosthetics and Brain Mind Institute, Faculty of Life Sciences, Swiss Federal Institute of Technology (EPFL), 1202, Switzerland
| | - Dorian F A Garin
- Department of Neurosurgery, University Hospitals of Geneva and Faculty of Medicine, University of Geneva, 1205, Switzerland
| | - Karl Schaller
- Department of Neurosurgery, University Hospitals of Geneva and Faculty of Medicine, University of Geneva, 1205, Switzerland
| | - Olaf Blanke
- Laboratory of Cognitive Neuroscience, Center for Neuroprosthetics and Brain Mind Institute, Faculty of Life Sciences, Swiss Federal Institute of Technology (EPFL), 1202, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, 1202, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne and Geneva, 1015, Switzerland
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35
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Shang S, Li G, Lin L. A method of source localization for bioelectricity based on “Orthogonal Differential Potential”. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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36
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Making ERP research more transparent: Guidelines for preregistration. Int J Psychophysiol 2021; 164:52-63. [PMID: 33676957 DOI: 10.1016/j.ijpsycho.2021.02.016] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 02/17/2021] [Accepted: 02/18/2021] [Indexed: 12/16/2022]
Abstract
A combination of confirmation bias, hindsight bias, and pressure to publish may prompt the (unconscious) exploration of various methodological options and reporting only the ones that lead to a (statistically) significant outcome. This undisclosed analytic flexibility is particularly relevant in EEG research, where a myriad of preprocessing and analysis pipelines can be used to extract information from complex multidimensional data. One solution to limit confirmation and hindsight bias by disclosing analytic choices is preregistration: researchers write a time-stamped, publicly accessible research plan with hypotheses, data collection plan, and the intended preprocessing and statistical analyses before the start of a research project. In this manuscript, we present an overview of the problems associated with undisclosed analytic flexibility, discuss why and how EEG researchers would benefit from adopting preregistration, provide guidelines and examples on how to preregister data preprocessing and analysis steps in typical ERP studies, and conclude by discussing possibilities and limitations of this open science practice.
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